** I-GUIDE Spatial AI Challenge 2025-26 -- Honorable Mention **

Our project used spatial AI and satellite Earth Observation to estimate the total volume of water applied for irrigation, addressing a critical gap in sustainable water resource management. Irrigation is the largest agricultural water use, yet total withdrawal volumes and their contributions to groundwater recharge remain poorly quantified especially in developing regions where irrigation supports food and water security. Existing remote sensing methods often miss deep percolation losses. To overcome this, the we integrated satellite embeddings, spatial AI models, and grid-based geospatial hydrological frameworks. Initial results show strong performance in estimating irrigation volumes, especially over non-irrigated areas. Upcoming validation at two Colorado pilot sites will refine deep percolation estimates. The model enables large-scale irrigation monitoring, enhancing understanding of human-environment interactions and supporting evidence-based water sustainability planning across socio-economic and environmental contexts.